I am trying to add constraints to a maximization problem. Let’s say I have a dataframe like this:

df = pd.DataFrame({'id':[1,2,3,4,5,6,7,8,9,10], 'somethingElse':['a','b','c','d','e','f','g','h','i','j'], 'car_type':['honda','honda','bmw','honda','vw','vw','kia','ferrari','mercedes','honda']
df = df.set_index(['id','somethingElse'])

In this problem, either a row gets picked or not. I have created flags:

flag = LpVariable.dicts('flag', ((id, somethingElse) for id, somethingElse in df.idnex), cat = 'Binary')

But I’m confused how to add constraints for something like “The % of Hondas selected should be 20% (or 18-22%)”. I’ve tried starting with something like:

lpSum([(flag[i[0], i[1])]*df.loc[(i[0], i[1]), 'car_type'] for i in df.index])

but it’s not working. Any help would be appreciated!

In this problem, either a row gets picked or not. I have created flags:

flag = LpVariable.dicts('flag', ((id, somethingElse) for id, somethingElse in df.idnex), cat = 'Binary')

But I’m confused how to add constraints for something like “The % of Hondas selected should be 20% (or 18-22%)”. I’ve tried starting with something like:

lpSum([(flag[i[0], i[1])]*df.loc[(i[0], i[1]), 'car_type'] for i in df.index])

but it’s not working. Any help would be appreciated!